{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:10.988026Z",
     "start_time": "2019-02-21T05:25:10.981809Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:19.958411Z",
     "start_time": "2019-02-21T05:25:11.605626Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('../../Kaggle_data/santander-customer-transaction-prediction/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:28.320046Z",
     "start_time": "2019-02-21T05:25:20.454458Z"
    }
   },
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('../../Kaggle_data/santander-customer-transaction-prediction/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:28.774992Z",
     "start_time": "2019-02-21T05:25:28.759624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    179902\n",
       "1     20098\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:32.828556Z",
     "start_time": "2019-02-21T05:25:29.226191Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
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       "      <th>var_8</th>\n",
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       "      <th>var_190</th>\n",
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       "      <th>var_192</th>\n",
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       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
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       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.100490</td>\n",
       "      <td>10.679914</td>\n",
       "      <td>-1.627622</td>\n",
       "      <td>10.715192</td>\n",
       "      <td>6.796529</td>\n",
       "      <td>11.078333</td>\n",
       "      <td>-5.065317</td>\n",
       "      <td>5.408949</td>\n",
       "      <td>16.545850</td>\n",
       "      <td>0.284162</td>\n",
       "      <td>...</td>\n",
       "      <td>3.234440</td>\n",
       "      <td>7.438408</td>\n",
       "      <td>1.927839</td>\n",
       "      <td>3.331774</td>\n",
       "      <td>17.993784</td>\n",
       "      <td>-0.142088</td>\n",
       "      <td>2.303335</td>\n",
       "      <td>8.908158</td>\n",
       "      <td>15.870720</td>\n",
       "      <td>-3.326537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.300653</td>\n",
       "      <td>3.040051</td>\n",
       "      <td>4.050044</td>\n",
       "      <td>2.640894</td>\n",
       "      <td>2.043319</td>\n",
       "      <td>1.623150</td>\n",
       "      <td>7.863267</td>\n",
       "      <td>0.866607</td>\n",
       "      <td>3.418076</td>\n",
       "      <td>3.332634</td>\n",
       "      <td>...</td>\n",
       "      <td>4.559922</td>\n",
       "      <td>3.023272</td>\n",
       "      <td>1.478423</td>\n",
       "      <td>3.992030</td>\n",
       "      <td>3.135162</td>\n",
       "      <td>1.429372</td>\n",
       "      <td>5.454369</td>\n",
       "      <td>0.921625</td>\n",
       "      <td>3.010945</td>\n",
       "      <td>10.438015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408400</td>\n",
       "      <td>-15.043400</td>\n",
       "      <td>2.117100</td>\n",
       "      <td>-0.040200</td>\n",
       "      <td>5.074800</td>\n",
       "      <td>-32.562600</td>\n",
       "      <td>2.347300</td>\n",
       "      <td>5.349700</td>\n",
       "      <td>-10.505500</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.093300</td>\n",
       "      <td>-2.691700</td>\n",
       "      <td>-3.814500</td>\n",
       "      <td>-11.783400</td>\n",
       "      <td>8.694400</td>\n",
       "      <td>-5.261000</td>\n",
       "      <td>-14.209600</td>\n",
       "      <td>5.960600</td>\n",
       "      <td>6.299300</td>\n",
       "      <td>-38.852800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.453850</td>\n",
       "      <td>-4.740025</td>\n",
       "      <td>8.722475</td>\n",
       "      <td>5.254075</td>\n",
       "      <td>9.883175</td>\n",
       "      <td>-11.200350</td>\n",
       "      <td>4.767700</td>\n",
       "      <td>13.943800</td>\n",
       "      <td>-2.317800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058825</td>\n",
       "      <td>5.157400</td>\n",
       "      <td>0.889775</td>\n",
       "      <td>0.584600</td>\n",
       "      <td>15.629800</td>\n",
       "      <td>-1.170700</td>\n",
       "      <td>-1.946925</td>\n",
       "      <td>8.252800</td>\n",
       "      <td>13.829700</td>\n",
       "      <td>-11.208475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.524750</td>\n",
       "      <td>-1.608050</td>\n",
       "      <td>10.580000</td>\n",
       "      <td>6.825000</td>\n",
       "      <td>11.108250</td>\n",
       "      <td>-4.833150</td>\n",
       "      <td>5.385100</td>\n",
       "      <td>16.456800</td>\n",
       "      <td>0.393700</td>\n",
       "      <td>...</td>\n",
       "      <td>3.203600</td>\n",
       "      <td>7.347750</td>\n",
       "      <td>1.901300</td>\n",
       "      <td>3.396350</td>\n",
       "      <td>17.957950</td>\n",
       "      <td>-0.172700</td>\n",
       "      <td>2.408900</td>\n",
       "      <td>8.888200</td>\n",
       "      <td>15.934050</td>\n",
       "      <td>-2.819550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.758200</td>\n",
       "      <td>1.358625</td>\n",
       "      <td>12.516700</td>\n",
       "      <td>8.324100</td>\n",
       "      <td>12.261125</td>\n",
       "      <td>0.924800</td>\n",
       "      <td>6.003000</td>\n",
       "      <td>19.102900</td>\n",
       "      <td>2.937900</td>\n",
       "      <td>...</td>\n",
       "      <td>6.406200</td>\n",
       "      <td>9.512525</td>\n",
       "      <td>2.949500</td>\n",
       "      <td>6.205800</td>\n",
       "      <td>20.396525</td>\n",
       "      <td>0.829600</td>\n",
       "      <td>6.556725</td>\n",
       "      <td>9.593300</td>\n",
       "      <td>18.064725</td>\n",
       "      <td>4.836800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.315000</td>\n",
       "      <td>10.376800</td>\n",
       "      <td>19.353000</td>\n",
       "      <td>13.188300</td>\n",
       "      <td>16.671400</td>\n",
       "      <td>17.251600</td>\n",
       "      <td>8.447700</td>\n",
       "      <td>27.691800</td>\n",
       "      <td>10.151300</td>\n",
       "      <td>...</td>\n",
       "      <td>18.440900</td>\n",
       "      <td>16.716500</td>\n",
       "      <td>8.402400</td>\n",
       "      <td>18.281800</td>\n",
       "      <td>27.928800</td>\n",
       "      <td>4.272900</td>\n",
       "      <td>18.321500</td>\n",
       "      <td>12.000400</td>\n",
       "      <td>26.079100</td>\n",
       "      <td>28.500700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              target          var_0          var_1          var_2  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.100490      10.679914      -1.627622      10.715192   \n",
       "std         0.300653       3.040051       4.050044       2.640894   \n",
       "min         0.000000       0.408400     -15.043400       2.117100   \n",
       "25%         0.000000       8.453850      -4.740025       8.722475   \n",
       "50%         0.000000      10.524750      -1.608050      10.580000   \n",
       "75%         0.000000      12.758200       1.358625      12.516700   \n",
       "max         1.000000      20.315000      10.376800      19.353000   \n",
       "\n",
       "               var_3          var_4          var_5          var_6  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        6.796529      11.078333      -5.065317       5.408949   \n",
       "std         2.043319       1.623150       7.863267       0.866607   \n",
       "min        -0.040200       5.074800     -32.562600       2.347300   \n",
       "25%         5.254075       9.883175     -11.200350       4.767700   \n",
       "50%         6.825000      11.108250      -4.833150       5.385100   \n",
       "75%         8.324100      12.261125       0.924800       6.003000   \n",
       "max        13.188300      16.671400      17.251600       8.447700   \n",
       "\n",
       "               var_7          var_8      ...              var_190  \\\n",
       "count  200000.000000  200000.000000      ...        200000.000000   \n",
       "mean       16.545850       0.284162      ...             3.234440   \n",
       "std         3.418076       3.332634      ...             4.559922   \n",
       "min         5.349700     -10.505500      ...           -14.093300   \n",
       "25%        13.943800      -2.317800      ...            -0.058825   \n",
       "50%        16.456800       0.393700      ...             3.203600   \n",
       "75%        19.102900       2.937900      ...             6.406200   \n",
       "max        27.691800      10.151300      ...            18.440900   \n",
       "\n",
       "             var_191        var_192        var_193        var_194  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        7.438408       1.927839       3.331774      17.993784   \n",
       "std         3.023272       1.478423       3.992030       3.135162   \n",
       "min        -2.691700      -3.814500     -11.783400       8.694400   \n",
       "25%         5.157400       0.889775       0.584600      15.629800   \n",
       "50%         7.347750       1.901300       3.396350      17.957950   \n",
       "75%         9.512525       2.949500       6.205800      20.396525   \n",
       "max        16.716500       8.402400      18.281800      27.928800   \n",
       "\n",
       "             var_195        var_196        var_197        var_198  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -0.142088       2.303335       8.908158      15.870720   \n",
       "std         1.429372       5.454369       0.921625       3.010945   \n",
       "min        -5.261000     -14.209600       5.960600       6.299300   \n",
       "25%        -1.170700      -1.946925       8.252800      13.829700   \n",
       "50%        -0.172700       2.408900       8.888200      15.934050   \n",
       "75%         0.829600       6.556725       9.593300      18.064725   \n",
       "max         4.272900      18.321500      12.000400      26.079100   \n",
       "\n",
       "             var_199  \n",
       "count  200000.000000  \n",
       "mean       -3.326537  \n",
       "std        10.438015  \n",
       "min       -38.852800  \n",
       "25%       -11.208475  \n",
       "50%        -2.819550  \n",
       "75%         4.836800  \n",
       "max        28.500700  \n",
       "\n",
       "[8 rows x 201 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:36.563191Z",
     "start_time": "2019-02-21T05:25:33.262865Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var_0</th>\n",
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       "      <td>200000.000000</td>\n",
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       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
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       "      <td>200000.000000</td>\n",
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       "      <td>200000.000000</td>\n",
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       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.658737</td>\n",
       "      <td>-1.624244</td>\n",
       "      <td>10.707452</td>\n",
       "      <td>6.788214</td>\n",
       "      <td>11.076399</td>\n",
       "      <td>-5.050558</td>\n",
       "      <td>5.415164</td>\n",
       "      <td>16.529143</td>\n",
       "      <td>0.277135</td>\n",
       "      <td>7.569407</td>\n",
       "      <td>...</td>\n",
       "      <td>3.189766</td>\n",
       "      <td>7.458269</td>\n",
       "      <td>1.925944</td>\n",
       "      <td>3.322016</td>\n",
       "      <td>17.996967</td>\n",
       "      <td>-0.133657</td>\n",
       "      <td>2.290899</td>\n",
       "      <td>8.912428</td>\n",
       "      <td>15.869184</td>\n",
       "      <td>-3.246342</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.036716</td>\n",
       "      <td>4.040509</td>\n",
       "      <td>2.633888</td>\n",
       "      <td>2.052724</td>\n",
       "      <td>1.616456</td>\n",
       "      <td>7.869293</td>\n",
       "      <td>0.864686</td>\n",
       "      <td>3.424482</td>\n",
       "      <td>3.333375</td>\n",
       "      <td>1.231865</td>\n",
       "      <td>...</td>\n",
       "      <td>4.551239</td>\n",
       "      <td>3.025189</td>\n",
       "      <td>1.479966</td>\n",
       "      <td>3.995599</td>\n",
       "      <td>3.140652</td>\n",
       "      <td>1.429678</td>\n",
       "      <td>5.446346</td>\n",
       "      <td>0.920904</td>\n",
       "      <td>3.008717</td>\n",
       "      <td>10.398589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.188700</td>\n",
       "      <td>-15.043400</td>\n",
       "      <td>2.355200</td>\n",
       "      <td>-0.022400</td>\n",
       "      <td>5.484400</td>\n",
       "      <td>-27.767000</td>\n",
       "      <td>2.216400</td>\n",
       "      <td>5.713700</td>\n",
       "      <td>-9.956000</td>\n",
       "      <td>4.243300</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.093300</td>\n",
       "      <td>-2.407000</td>\n",
       "      <td>-3.340900</td>\n",
       "      <td>-11.413100</td>\n",
       "      <td>9.382800</td>\n",
       "      <td>-4.911900</td>\n",
       "      <td>-13.944200</td>\n",
       "      <td>6.169600</td>\n",
       "      <td>6.584000</td>\n",
       "      <td>-39.457800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.442975</td>\n",
       "      <td>-4.700125</td>\n",
       "      <td>8.735600</td>\n",
       "      <td>5.230500</td>\n",
       "      <td>9.891075</td>\n",
       "      <td>-11.201400</td>\n",
       "      <td>4.772600</td>\n",
       "      <td>13.933900</td>\n",
       "      <td>-2.303900</td>\n",
       "      <td>6.623800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.095000</td>\n",
       "      <td>5.166500</td>\n",
       "      <td>0.882975</td>\n",
       "      <td>0.587600</td>\n",
       "      <td>15.634775</td>\n",
       "      <td>-1.160700</td>\n",
       "      <td>-1.948600</td>\n",
       "      <td>8.260075</td>\n",
       "      <td>13.847275</td>\n",
       "      <td>-11.124000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.513800</td>\n",
       "      <td>-1.590500</td>\n",
       "      <td>10.560700</td>\n",
       "      <td>6.822350</td>\n",
       "      <td>11.099750</td>\n",
       "      <td>-4.834100</td>\n",
       "      <td>5.391600</td>\n",
       "      <td>16.422700</td>\n",
       "      <td>0.372000</td>\n",
       "      <td>7.632000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.162400</td>\n",
       "      <td>7.379000</td>\n",
       "      <td>1.892600</td>\n",
       "      <td>3.428500</td>\n",
       "      <td>17.977600</td>\n",
       "      <td>-0.162000</td>\n",
       "      <td>2.403600</td>\n",
       "      <td>8.892800</td>\n",
       "      <td>15.943400</td>\n",
       "      <td>-2.725950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.739600</td>\n",
       "      <td>1.343400</td>\n",
       "      <td>12.495025</td>\n",
       "      <td>8.327600</td>\n",
       "      <td>12.253400</td>\n",
       "      <td>0.942575</td>\n",
       "      <td>6.005800</td>\n",
       "      <td>19.094550</td>\n",
       "      <td>2.930025</td>\n",
       "      <td>8.584825</td>\n",
       "      <td>...</td>\n",
       "      <td>6.336475</td>\n",
       "      <td>9.531100</td>\n",
       "      <td>2.956000</td>\n",
       "      <td>6.174200</td>\n",
       "      <td>20.391725</td>\n",
       "      <td>0.837900</td>\n",
       "      <td>6.519800</td>\n",
       "      <td>9.595900</td>\n",
       "      <td>18.045200</td>\n",
       "      <td>4.935400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.323400</td>\n",
       "      <td>9.385100</td>\n",
       "      <td>18.714100</td>\n",
       "      <td>13.142000</td>\n",
       "      <td>16.037100</td>\n",
       "      <td>17.253700</td>\n",
       "      <td>8.302500</td>\n",
       "      <td>28.292800</td>\n",
       "      <td>9.665500</td>\n",
       "      <td>11.003600</td>\n",
       "      <td>...</td>\n",
       "      <td>20.359000</td>\n",
       "      <td>16.716500</td>\n",
       "      <td>8.005000</td>\n",
       "      <td>17.632600</td>\n",
       "      <td>27.947800</td>\n",
       "      <td>4.545400</td>\n",
       "      <td>15.920700</td>\n",
       "      <td>12.275800</td>\n",
       "      <td>26.538400</td>\n",
       "      <td>27.907400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               var_0          var_1          var_2          var_3  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       10.658737      -1.624244      10.707452       6.788214   \n",
       "std         3.036716       4.040509       2.633888       2.052724   \n",
       "min         0.188700     -15.043400       2.355200      -0.022400   \n",
       "25%         8.442975      -4.700125       8.735600       5.230500   \n",
       "50%        10.513800      -1.590500      10.560700       6.822350   \n",
       "75%        12.739600       1.343400      12.495025       8.327600   \n",
       "max        22.323400       9.385100      18.714100      13.142000   \n",
       "\n",
       "               var_4          var_5          var_6          var_7  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       11.076399      -5.050558       5.415164      16.529143   \n",
       "std         1.616456       7.869293       0.864686       3.424482   \n",
       "min         5.484400     -27.767000       2.216400       5.713700   \n",
       "25%         9.891075     -11.201400       4.772600      13.933900   \n",
       "50%        11.099750      -4.834100       5.391600      16.422700   \n",
       "75%        12.253400       0.942575       6.005800      19.094550   \n",
       "max        16.037100      17.253700       8.302500      28.292800   \n",
       "\n",
       "               var_8          var_9      ...              var_190  \\\n",
       "count  200000.000000  200000.000000      ...        200000.000000   \n",
       "mean        0.277135       7.569407      ...             3.189766   \n",
       "std         3.333375       1.231865      ...             4.551239   \n",
       "min        -9.956000       4.243300      ...           -14.093300   \n",
       "25%        -2.303900       6.623800      ...            -0.095000   \n",
       "50%         0.372000       7.632000      ...             3.162400   \n",
       "75%         2.930025       8.584825      ...             6.336475   \n",
       "max         9.665500      11.003600      ...            20.359000   \n",
       "\n",
       "             var_191        var_192        var_193        var_194  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        7.458269       1.925944       3.322016      17.996967   \n",
       "std         3.025189       1.479966       3.995599       3.140652   \n",
       "min        -2.407000      -3.340900     -11.413100       9.382800   \n",
       "25%         5.166500       0.882975       0.587600      15.634775   \n",
       "50%         7.379000       1.892600       3.428500      17.977600   \n",
       "75%         9.531100       2.956000       6.174200      20.391725   \n",
       "max        16.716500       8.005000      17.632600      27.947800   \n",
       "\n",
       "             var_195        var_196        var_197        var_198  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -0.133657       2.290899       8.912428      15.869184   \n",
       "std         1.429678       5.446346       0.920904       3.008717   \n",
       "min        -4.911900     -13.944200       6.169600       6.584000   \n",
       "25%        -1.160700      -1.948600       8.260075      13.847275   \n",
       "50%        -0.162000       2.403600       8.892800      15.943400   \n",
       "75%         0.837900       6.519800       9.595900      18.045200   \n",
       "max         4.545400      15.920700      12.275800      26.538400   \n",
       "\n",
       "             var_199  \n",
       "count  200000.000000  \n",
       "mean       -3.246342  \n",
       "std        10.398589  \n",
       "min       -39.457800  \n",
       "25%       -11.124000  \n",
       "50%        -2.725950  \n",
       "75%         4.935400  \n",
       "max        27.907400  \n",
       "\n",
       "[8 rows x 200 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "target的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:37.182603Z",
     "start_time": "2019-02-21T05:25:37.008801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e407bd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:25:37.699493Z",
     "start_time": "2019-02-21T05:25:37.682838Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_feature_distribution(df1, df2, label1, label2, features):\n",
    "    i = 0\n",
    "    sns.set_style('whitegrid')\n",
    "    plt.figure()\n",
    "    fig, ax = plt.subplots(10,10,figsize=(18,22))\n",
    "\n",
    "    for feature in features:\n",
    "        i += 1\n",
    "        plt.subplot(10,10,i)\n",
    "        sns.kdeplot(df1[feature], bw=0.5,label=label1)\n",
    "        sns.kdeplot(df2[feature], bw=0.5,label=label2)\n",
    "        plt.xlabel(feature, fontsize=9)\n",
    "        locs, labels = plt.xticks()\n",
    "        plt.tick_params(axis='x', which='major', labelsize=6, pad=-6)\n",
    "        plt.tick_params(axis='y', which='major', labelsize=6)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:24:32.295357Z",
     "start_time": "2019-02-21T05:24:31.313518Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-57a76b8cfc69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m102\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplot_feature_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'0'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-9-daf9194519d9>\u001b[0m in \u001b[0;36mplot_feature_distribution\u001b[0;34m(df1, df2, label1, label2, features)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'whitegrid'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m22\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "t0 = train_df.loc[train_df['target'] == 0]\n",
    "t1 = train_df.loc[train_df['target'] == 1]\n",
    "features = train_df.columns.values[2:102]\n",
    "plot_feature_distribution(t0, t1, '0', '1', features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:24:32.299625Z",
     "start_time": "2019-02-21T05:24:07.429Z"
    }
   },
   "outputs": [],
   "source": [
    "corrmat = train_df.corr().abs()\n",
    "\n",
    "k = 20\n",
    "cols = corrmat.nlargest(k, 'target').index\n",
    "cm = np.corrcoef(train_df[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "f, ax = plt.subplots(figsize=(16, 8))\n",
    "sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size':10}, yticklabels=cols.values, xticklabels=cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:24:32.302431Z",
     "start_time": "2019-02-21T05:24:07.520Z"
    }
   },
   "outputs": [],
   "source": [
    "correlations = train_df[features].corr().abs().unstack().sort_values(kind=\"quicksort\").reset_index()\n",
    "correlations = correlations[correlations['level_0'] != correlations['level_1']]\n",
    "correlations.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:24:32.311827Z",
     "start_time": "2019-02-21T05:24:07.987Z"
    }
   },
   "outputs": [],
   "source": [
    "correlations.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 每列的重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:24:32.318414Z",
     "start_time": "2019-02-21T05:24:08.359Z"
    }
   },
   "outputs": [],
   "source": [
    "features = train_df.columns.values[2:202]\n",
    "unique_max_train = []\n",
    "unique_max_test = []\n",
    "for feature in features:\n",
    "    values = train_df[feature].value_counts()\n",
    "    unique_max_train.append([feature, values.max(), values.idxmax()])\n",
    "    values = test_df[feature].value_counts()\n",
    "    unique_max_test.append([feature, values.max(), values.idxmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:24:32.325188Z",
     "start_time": "2019-02-21T05:24:08.536Z"
    }
   },
   "outputs": [],
   "source": [
    "np.transpose((pd.DataFrame(unique_max_train, columns=['Feature', 'Max duplicates', 'Value'])).\\\n",
    "            sort_values(by = 'Max duplicates', ascending=False).head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:24:32.333452Z",
     "start_time": "2019-02-21T05:24:08.696Z"
    }
   },
   "outputs": [],
   "source": [
    "np.transpose((pd.DataFrame(unique_max_test, columns=['Feature', 'Max duplicates', 'Value'])).\\\n",
    "            sort_values(by = 'Max duplicates', ascending=False).head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通用聚合方法（sum, min, max...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:09.397432Z",
     "start_time": "2019-02-21T05:25:38.118055Z"
    }
   },
   "outputs": [],
   "source": [
    "i = 1\n",
    "for df in [test_df, train_df]:\n",
    "    idx = df.columns.values[i:i+200]\n",
    "    df['sum'] = df[idx].sum(axis=1)  \n",
    "    df['min'] = df[idx].min(axis=1)\n",
    "    df['max'] = df[idx].max(axis=1)\n",
    "    df['mean'] = df[idx].mean(axis=1)\n",
    "    df['std'] = df[idx].std(axis=1)\n",
    "    df['skew'] = df[idx].skew(axis=1)\n",
    "    df['kurt'] = df[idx].kurtosis(axis=1)\n",
    "    df['med'] = df[idx].median(axis=1)\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:10.406090Z",
     "start_time": "2019-02-21T05:26:10.013428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurt</th>\n",
       "      <th>med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1456.3182</td>\n",
       "      <td>-21.4494</td>\n",
       "      <td>43.1127</td>\n",
       "      <td>7.281591</td>\n",
       "      <td>9.331540</td>\n",
       "      <td>0.101580</td>\n",
       "      <td>1.331023</td>\n",
       "      <td>6.77040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1415.3636</td>\n",
       "      <td>-47.3797</td>\n",
       "      <td>40.5632</td>\n",
       "      <td>7.076818</td>\n",
       "      <td>10.336130</td>\n",
       "      <td>-0.351734</td>\n",
       "      <td>4.110215</td>\n",
       "      <td>7.22315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1240.8966</td>\n",
       "      <td>-22.4038</td>\n",
       "      <td>33.8820</td>\n",
       "      <td>6.204483</td>\n",
       "      <td>8.753387</td>\n",
       "      <td>-0.056957</td>\n",
       "      <td>0.546438</td>\n",
       "      <td>5.89940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1288.2319</td>\n",
       "      <td>-35.1659</td>\n",
       "      <td>38.1015</td>\n",
       "      <td>6.441160</td>\n",
       "      <td>9.594064</td>\n",
       "      <td>-0.480116</td>\n",
       "      <td>2.630499</td>\n",
       "      <td>6.70260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1354.2310</td>\n",
       "      <td>-65.4863</td>\n",
       "      <td>41.1037</td>\n",
       "      <td>6.771155</td>\n",
       "      <td>11.287122</td>\n",
       "      <td>-1.463426</td>\n",
       "      <td>9.787399</td>\n",
       "      <td>6.94735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sum      min      max      mean        std      skew      kurt  \\\n",
       "0  1456.3182 -21.4494  43.1127  7.281591   9.331540  0.101580  1.331023   \n",
       "1  1415.3636 -47.3797  40.5632  7.076818  10.336130 -0.351734  4.110215   \n",
       "2  1240.8966 -22.4038  33.8820  6.204483   8.753387 -0.056957  0.546438   \n",
       "3  1288.2319 -35.1659  38.1015  6.441160   9.594064 -0.480116  2.630499   \n",
       "4  1354.2310 -65.4863  41.1037  6.771155  11.287122 -1.463426  9.787399   \n",
       "\n",
       "       med  \n",
       "0  6.77040  \n",
       "1  7.22315  \n",
       "2  5.89940  \n",
       "3  6.70260  \n",
       "4  6.94735  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df.columns[202:]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:11.297898Z",
     "start_time": "2019-02-21T05:26:10.913488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurt</th>\n",
       "      <th>med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1416.6404</td>\n",
       "      <td>-31.9891</td>\n",
       "      <td>42.0248</td>\n",
       "      <td>7.083202</td>\n",
       "      <td>9.910632</td>\n",
       "      <td>-0.088518</td>\n",
       "      <td>1.871262</td>\n",
       "      <td>7.31440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1249.6860</td>\n",
       "      <td>-41.1924</td>\n",
       "      <td>35.6020</td>\n",
       "      <td>6.248430</td>\n",
       "      <td>9.541267</td>\n",
       "      <td>-0.559785</td>\n",
       "      <td>3.391068</td>\n",
       "      <td>6.43960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1430.2599</td>\n",
       "      <td>-34.3488</td>\n",
       "      <td>39.3654</td>\n",
       "      <td>7.151300</td>\n",
       "      <td>9.967466</td>\n",
       "      <td>-0.135084</td>\n",
       "      <td>2.326901</td>\n",
       "      <td>7.26355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1411.4447</td>\n",
       "      <td>-21.4797</td>\n",
       "      <td>40.3383</td>\n",
       "      <td>7.057224</td>\n",
       "      <td>8.257204</td>\n",
       "      <td>-0.167741</td>\n",
       "      <td>2.253054</td>\n",
       "      <td>6.89675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1423.7364</td>\n",
       "      <td>-24.8254</td>\n",
       "      <td>45.5510</td>\n",
       "      <td>7.118682</td>\n",
       "      <td>10.043542</td>\n",
       "      <td>0.293484</td>\n",
       "      <td>2.044943</td>\n",
       "      <td>6.83375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sum      min      max      mean        std      skew      kurt  \\\n",
       "0  1416.6404 -31.9891  42.0248  7.083202   9.910632 -0.088518  1.871262   \n",
       "1  1249.6860 -41.1924  35.6020  6.248430   9.541267 -0.559785  3.391068   \n",
       "2  1430.2599 -34.3488  39.3654  7.151300   9.967466 -0.135084  2.326901   \n",
       "3  1411.4447 -21.4797  40.3383  7.057224   8.257204 -0.167741  2.253054   \n",
       "4  1423.7364 -24.8254  45.5510  7.118682  10.043542  0.293484  2.044943   \n",
       "\n",
       "       med  \n",
       "0  7.31440  \n",
       "1  6.43960  \n",
       "2  7.26355  \n",
       "3  6.89675  \n",
       "4  6.83375  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df[test_df.columns[201:]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 新特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:11.851544Z",
     "start_time": "2019-02-21T05:26:11.834729Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_new_feature_distribution(df1, df2, label1, label2, features):\n",
    "    i = 0\n",
    "    sns.set_style('whitegrid')\n",
    "    plt.figure()\n",
    "    fig, ax = plt.subplots(2,4,figsize=(18,8))\n",
    "\n",
    "    for feature in features:\n",
    "        i += 1\n",
    "        plt.subplot(2,4,i)\n",
    "        sns.kdeplot(df1[feature], bw=0.5,label=label1)\n",
    "        sns.kdeplot(df2[feature], bw=0.5,label=label2)\n",
    "        plt.xlabel(feature, fontsize=11)\n",
    "        locs, labels = plt.xticks()\n",
    "        plt.tick_params(axis='x', which='major', labelsize=8)\n",
    "        plt.tick_params(axis='y', which='major', labelsize=8)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:14.289265Z",
     "start_time": "2019-02-21T05:26:12.368172Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t0 = train_df.loc[train_df['target'] == 0]\n",
    "t1 = train_df.loc[train_df['target'] == 1]\n",
    "features = train_df.columns.values[202:]\n",
    "plot_new_feature_distribution(t0, t1, 'target: 0', 'target: 1', features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:17.127373Z",
     "start_time": "2019-02-21T05:26:15.222876Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = train_df.columns.values[202:]\n",
    "plot_new_feature_distribution(train_df, test_df, 'train', 'test', features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:13:23.286082Z",
     "start_time": "2019-02-21T05:13:23.020244Z"
    }
   },
   "source": [
    "## 特征中全为小数 => 增加保留1位，保留2位特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:28.958944Z",
     "start_time": "2019-02-21T05:26:18.018452Z"
    }
   },
   "outputs": [],
   "source": [
    "features = [c for c in train_df.columns if c not in ['ID_code', 'target']]\n",
    "for feature in features:\n",
    "    train_df['r2_'+feature] = np.round(train_df[feature], 2)\n",
    "    test_df['r2_'+feature] = np.round(test_df[feature], 2)\n",
    "    train_df['r1_'+feature] = np.round(train_df[feature], 1)\n",
    "    test_df['r1_'+feature] = np.round(test_df[feature], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-05T05:16:37.833830Z",
     "start_time": "2019-03-05T05:16:37.688717Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-ef797c2b3b5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'train_df' is not defined"
     ]
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:30.366868Z",
     "start_time": "2019-02-21T05:26:30.341273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 626)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除无关特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:31.290795Z",
     "start_time": "2019-02-21T05:26:31.274038Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'ID_code', u'target', u'var_0', u'var_1', u'var_2', u'var_3', u'var_4',\n",
       "       u'var_5', u'var_6', u'var_7',\n",
       "       ...\n",
       "       u'r2_mean', u'r1_mean', u'r2_std', u'r1_std', u'r2_skew', u'r1_skew',\n",
       "       u'r2_kurt', u'r1_kurt', u'r2_med', u'r1_med'],\n",
       "      dtype='object', length=626)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:34.448849Z",
     "start_time": "2019-02-21T05:26:32.306500Z"
    }
   },
   "outputs": [],
   "source": [
    "features_train = train_df.drop(['ID_code', 'target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:35.105293Z",
     "start_time": "2019-02-21T05:26:35.099292Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = train_df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:36.059492Z",
     "start_time": "2019-02-21T05:26:36.036875Z"
    }
   },
   "outputs": [],
   "source": [
    "features = [c for c in train_df.columns if c not in ['ID_code', 'target']]\n",
    "target = train_df['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机欠采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:36.881306Z",
     "start_time": "2019-02-21T05:26:36.871648Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# from collections import Counter\n",
    "# from sklearn.datasets import make_classification\n",
    "# from imblearn.under_sampling import RandomUnderSampler\n",
    "# from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# # rus = RandomUnderSampler(random_state=42)\n",
    "# # X1, y1 = rus.fit_sample(features_train, labels)\n",
    "\n",
    "# rus = RandomOverSampler(random_state=42)\n",
    "# X1, y1 = rus.fit_sample(features_train, labels)\n",
    "\n",
    "# print('Under Resampled dataset shape {}'.format(len(y1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:37.874199Z",
     "start_time": "2019-02-21T05:26:37.588963Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tugame/miniconda3/envs/py27/lib/python2.7/site-packages/lightgbm/__init__.py:46: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_9.4.1) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "  \"You can install the OpenMP library by the following command: ``brew install libomp``.\", UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:26:44.127604Z",
     "start_time": "2019-02-21T05:26:44.113346Z"
    }
   },
   "outputs": [],
   "source": [
    "param = {\n",
    "        'num_leaves': 5,\n",
    "        'max_bin': 63,\n",
    "        'min_data_in_leaf': 45,\n",
    "        'learning_rate': 0.01,\n",
    "        'min_sum_hessian_in_leaf': 0.000446,\n",
    "        'bagging_fraction': 0.55, \n",
    "        'bagging_freq': 5, \n",
    "        'max_depth': 14,\n",
    "        'save_binary': True,\n",
    "        'seed': 31452,\n",
    "        'feature_fraction_seed': 31415,\n",
    "         'feature_fraction': 0.51,\n",
    "        'bagging_seed': 31415,\n",
    "        'drop_seed': 31415,\n",
    "        'data_random_seed': 31415,\n",
    "        'objective': 'binary',\n",
    "        'boosting_type': 'gbdt',\n",
    "        'verbose': 1,\n",
    "        'metric': 'auc',\n",
    "        'is_unbalance': True,\n",
    "        'boost_from_average': False,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-21T05:27:28.902520Z",
     "start_time": "2019-02-21T05:27:28.746349Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'StratifiedKFold' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-1fb7fdeb5d4b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfolds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStratifiedKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0moof\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfeature_importance_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'StratifiedKFold' is not defined"
     ]
    }
   ],
   "source": [
    "folds = StratifiedKFold(n_splits=9, shuffle=True, random_state=42)\n",
    "oof = np.zeros(len(train_df))\n",
    "predictions = np.zeros(len(test_df))\n",
    "feature_importance_df = pd.DataFrame()\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_df.values, target.values)):\n",
    "    print(\"Fold {}\".format(fold_))\n",
    "    trn_data = lgb.Dataset(train_df.iloc[trn_idx][features], label=target.iloc[trn_idx])\n",
    "    val_data = lgb.Dataset(train_df.iloc[val_idx][features], label=target.iloc[val_idx])\n",
    "\n",
    "    num_round = 15000\n",
    "    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=1000, early_stopping_rounds = 250)\n",
    "    oof[val_idx] = clf.predict(train_df.iloc[val_idx][features], num_iteration=clf.best_iteration)\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"Feature\"] = features\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importance()\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    \n",
    "    predictions += clf.predict(test_df[features], num_iteration=clf.best_iteration) / folds.n_splits\n",
    "\n",
    "print(\"CV score: {:<8.5f}\".format(roc_auc_score(target, oof)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型的特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = (feature_importance_df[[\"Feature\", \"importance\"]]\n",
    "        .groupby(\"Feature\")\n",
    "        .mean()\n",
    "        .sort_values(by=\"importance\", ascending=False)[:150].index)\n",
    "best_features = feature_importance_df.loc[feature_importance_df.Feature.isin(cols)]\n",
    "\n",
    "plt.figure(figsize=(14,28))\n",
    "sns.barplot(x=\"importance\", y=\"Feature\", data=best_features.sort_values(by=\"importance\",ascending=False))\n",
    "plt.title('Features importance (averaged/folds)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('FI.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:15:50.615306Z",
     "start_time": "2019-02-19T05:15:50.181885Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X1, y1, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:15:51.031396Z",
     "start_time": "2019-02-19T05:15:51.019371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(287843, 71961)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:33:30.773078Z",
     "start_time": "2019-02-19T05:15:51.854299Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.7801579333178156,\n",
       " 'RandomForestClassifier': 0.6799435803545683,\n",
       " 'XGBClassifier': 0.7774099074842883}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, make_scorer, auc\n",
    "\n",
    "\n",
    "clf1 = LogisticRegression()\n",
    "clf2 = RandomForestClassifier(max_depth=5)\n",
    "clf3 = XGBClassifier()\n",
    "clf4 = SVC(kernel='rbf')\n",
    "\n",
    "results = {}\n",
    "for clf in [clf1, clf2, clf3]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    clf.fit(X_train, y_train)\n",
    "    pred_test = clf.predict(X_train)\n",
    "    results[clf_name] = accuracy_score(pred_test, y_train)\n",
    "    \n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:33:31.804897Z",
     "start_time": "2019-02-19T05:33:31.135289Z"
    }
   },
   "outputs": [],
   "source": [
    "for clf in [clf1, clf2, clf3]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    pred_val = clf.predict(X_val)\n",
    "    results[clf_name] = accuracy_score(pred_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:33:32.167975Z",
     "start_time": "2019-02-19T05:33:32.158079Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.7805755895554537,\n",
       " 'RandomForestClassifier': 0.677491974819694,\n",
       " 'XGBClassifier': 0.7727102180347688}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:34:33.106687Z",
     "start_time": "2019-02-19T05:34:33.096568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:34:51.903138Z",
     "start_time": "2019-02-19T05:34:49.826003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "probas = clf3.predict_proba(features_train.values)\n",
    "probas = probas[:,0]\n",
    "\n",
    "y = labels\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y, probas, pos_label=0)\n",
    "\n",
    "roc_auc=metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.clf()\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:34:52.397341Z",
     "start_time": "2019-02-19T05:34:52.387408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8570174369124104"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测提交"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:09:43.963481Z",
     "start_time": "2019-02-19T05:09:43.957881Z"
    }
   },
   "outputs": [],
   "source": [
    "test_codes = test_df['ID_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:09:44.536546Z",
     "start_time": "2019-02-19T05:09:44.305508Z"
    }
   },
   "outputs": [],
   "source": [
    "features_test = test_df.drop(['ID_code'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:09:45.003269Z",
     "start_time": "2019-02-19T05:09:44.929293Z"
    }
   },
   "outputs": [],
   "source": [
    "labels_test = clf1.predict(features_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:09:45.447286Z",
     "start_time": "2019-02-19T05:09:45.389238Z"
    }
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({'ID_code': test_codes, 'target': labels_test}, index=range(0, len(labels_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T05:09:46.698736Z",
     "start_time": "2019-02-19T05:09:45.873970Z"
    }
   },
   "outputs": [],
   "source": [
    "submission.to_csv('result.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "1、metadata + LR => ROC:0.779\n",
    "\n",
    "2、feature_tools"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "py27",
   "language": "python",
   "name": "py27"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "215.17px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
